Coordinated Active Voltage Control Strategy for Active Distribution Networks Based on Multi-Agent Actor-Critic with Multi-Head Attention

High penetration of distributed photovoltaic (PV) generation in active distribution networks (ADNs) has intensified voltage violations and rapid voltage fluctuations, especially under extreme reverse-power-flow conditions. Traditional centralized voltage regulation methods rely on accurate physical network parameters and wide-area communication, making it difficult to achieve fast online coordination under rapidly changing operating conditions. To address this issue, this paper proposes a coordinated active voltage control strategy for ADNs based on multi-agent actor-critic learning with a multi-head attention mechanism. The PV-cluster reactive power coordination problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and a reward function combining a bowl-shaped voltage barrier term, a voltage-stability safety term, and an equipment-utilization regularization term is designed. In addition, the multi-head attention mechanism is used to extract state-dependent decision relevance among PV agents, thereby reducing redundant information in high-dimensional state spaces. Case studies on IEEE 33-node and 141-node systems demonstrate that the proposed method outperforms both OPF and benchmark DRL methods in voltage regulation performance. Additional ablation, interpretability, and online-time analyses further verify the contributions of the attention module and the voltage barrier reward design.